A Novel Multi-objective Risk-informed Rehabilitation Framework for Sewerage Systems

Abstract

Stormwater sewer infrastructure is at risk due to ageing, structural deterioration, population growth, and climate change. Since the consequences of the sewer system failure can adversely impact the community safety, environment and economy, a resilient infrastructure system is of essential importance. However, limited reinvestment budget and insufficient asset management practices impact the rehabilitation of urban sewerage systems. Therefore, an effective and efficient rehabilitation plan is needed to help proper investment decisions. An effective rehabilitation plan will maximize hydraulic performance while minimizing the overall failure risk within a limited budget. The current study aims to address this issue through designing a risk-informed methodology in three steps. First, the hydraulic risk index (obtained using the SWMM model) was combined with the ageing pipe index. The framework uses multi-objective optimization technique to generate solutions under specific sewerage conditions. We named this new framework as Hydraulics and Risk Combined Model (HRCM). Several scenarios including high hydraulic risk, high ageing risk, hydraulic risk and ageing risk (combined problems), and limited budget problems, are used to test the performance of the proposed methodology. The results show that the proposed model could provide a satisfactory solution. Then, in order to increase the calculation speed and improve the accuracy, sensitivity and cost-effectiveness analyses were also conducted for the proposed methodology with different algorithms. The results show that different algorithms offer various benefits. A new calculation method was offered by combining the advantages of the previous methods. Finally, a new optimization method named Phenotype Searching Method, which was enlightened by sexual selection processes, was offered. This method can enhance the selection processes to specific phenotypes (pipes) so that it can increase the convergence speed and increase the performance of the HRCM model.